MoRL: Reinforced Reasoning for Unified Motion Understanding and Generation

Hongpeng Wang, Zeyu Zhang, Wenhao Li, Hao Tang

MoRL: Reinforced Reasoning for Unified Motion Understanding and Generation: 3 upvotes on Hugging Face Daily Papers, #21 of 36 papers on 2026-02-17. Day-by-day upvote history.

Human motion understanding and generation are crucial for vision and robotics but remain limited in reasoning capability and test-time planning. We propose MoRL, a unified multimodal motion model trained with supervised fine-tuning and reinforcement learning with verifiable rewards. Our task-specific reward design combines semantic alignment and reasoning coherence for understanding with physical plausibility and text-motion consistency for generation, improving both logical reasoning and perceptual realism. To further enhance inference, we introduce Chain-of-Motion (CoM), a test-time reasoning method that enables step-by-step planning and reflection. We also construct two large-scale CoT datasets, MoUnd-CoT-140K and MoGen-CoT-140K, to align motion sequences with reasoning traces and action descriptions. Experiments on HumanML3D and KIT-ML show that MoRL achieves significant gains over state-of-the-art baselines. Code: https://github.com/AIGeeksGroup/MoRL. Website: https://aigeeksgroup.github.io/MoRL.

Paper page on Hugging Face · arXiv

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